We conducted a preliminary assessment of the size and distribution of the shark tourism sector in Indonesia based on available data. We identified all known and potential sites for shark tourism based on (a) a review of published literature, (b) internet searches using key words e.g., “shark diving Indonesia,” “shark trips Indonesia,” “shark operations Indonesia,” “shark photography Indonesia,” “shark encounters Indonesia,” and (c) expert opinion and anecdotal information. We define shark and ray tourism sites as locations where recreational activities (such as scuba diving, snorkeling and boat trips) are undertaken to view sharks in their natural habitat without the intention to harm them. We only included sites with established commercial operations for said recreational activities, and where sharks are explicitly marketed as an attraction (e.g., on websites, brochures, blogs). These methods are consistent with other tourism valuation studies for sharks (Anderson et al., 2011; Gallagher and Hammerschlag, 2011; O’Malley et al., 2013).

This preliminary scoping identified 24 shark tourism sites (Figure 1), which varied across a wide range of inter-related characteristics, such as: geographic location, accessibility/travel effort, focal species as tourist attraction, available tourism activities (e.g., scuba diving, snorkeling, boat-based watching), seasonality and level of development of site/province.

We conducted a national-level online survey of dive operators via Survey Monkey (Supplementary Information) to obtain data on tour operations (including types of activities, prices, numbers of trips, and seasonality), business profiles and projections, the perceived importance of sharks for tourism, and the benefits of tourism operations for local people and the economy. We circulated the survey via email using contact details obtained during the scoping phase and by referral. During June-August 2017, we emailed 169 dive centers where 33 of them responded to the survey (19.5% response rate). Of these respondents, only 14 operators specified their area of operation. These 14 companies operate across four provinces: Bali (n = 7), West Nusa Tenggara (n = 2), East Nusa Tenggara (n = 2) and North Sulawesi (n = 3). We obtained province-specific estimations of shark tourism from these 14 sites.

A case study approach was adopted for primary data collection, combined with a meta-analytical benefit-transfer method for interpolating values to non-surveyed sites and extrapolating values to the national-level (Richardson et al., 2015). Benefit transfer is defined as “the transfer of original value estimates from an existing “study site” or multiple study sites to an unstudied “policy site” with similar characteristics that is being evaluated” (Richardson et al., 2015, p. 52). This method was deemed necessary because conducting a nation-wide survey of all tourism sites was unfeasible, yet local context is critical to understanding value of ecotourism and the role of a given site in achieving conservation outcomes (Balmford and Whitten, 2003).

To select case studies for primary data collection, the identified sites were grouped according to different case types, based on characteristics that might influence their role and relative value in the tourism industry, such as accessibility/travel effort from major airports in Indonesia, focal species for tourism attraction, and size of tourism industry. Practicality and seasonality were also taken in to consideration for selecting the final research sites. Out of the 24 sites (Figure 1), six sites were selected for primary data collection (Table 1). Values from these six sites were later transferred to other non-sampled sites to arrive at a national estimate of the economic benefits of shark and ray tourism.

A total of 24 shark tourism sites were identified in the scoping phase (Figure 1). A site in its infancy in terms of tourism development (Saleh Bay, West Nusa Tenggara) was excluded from our list. We surveyed tourists in six case study sites using interviewer-assisted questionnaires (Supplementary Information), i.e., Nusa Penida (Bali), Labuan Bajo (Flores, East Nusa Tenggara), Morotai (North Maluku), Bunaken (North Sulawesi), Gili Matra (Lombok), and Derawan (East Kalimantan). We specifically targeted tourists who had already been on a shark or ray tourism activity during their stay (herein “shark tourists”). The tourist questionnaires were divided into demographic questions, economic questions and tourist experience questions. Questions focused in particular on direct tourist expenditures (Stoeckl et al., 2010; Mustika et al., 2012), the perceived importance of sharks for their trip, and their overall trip experience and satisfaction.

Dive-related expenditures are classified as “primary direct expenditures” (after Mustika et al., 2012), of which values were obtained from the tourists themselves (in terms of cost per day per person and how many days they dived). We also asked each tourist how much their expenditures were per person per day on food and drinks, accommodation, communication (internet, phone, phone credit, SIM card), souvenirs, local transportation (vehicle rental, taxi, etc.) and other expenditures (which they had to specify). If the tourists came to the site with a package tour, we asked them to specify how much they paid for the total package. We identify these items as “auxiliary direct expenditures” (after Mustika et al., 2012), or “auxiliary expenditures” for brevity.

An important concept in this study is the hypothetical loss of income a site might suffer in the absence of sharks. This hypothetical loss is measured by expenditures attributable to sharks and/or rays sharks and/or rays spent by tourists who came to a location mainly due to see sharks and/or rays (based on Stoeckl et al., 2010; D’Lima et al., 2016; Mustika et al., 2018). To estimate the attributable expenditures, we asked tourists to state the hypothetical number of days they would have spent in a site if it wasn’t possible to see sharks. Tourists who stated that they would not come to the site in the absence of sharks/rays are classified as “dedicated shark tourists.” Tourists who stated that they would reduce their trip length in the absence of sharks/rays are classified “partially dedicated shark tourists.” The expenditures from dedicated and partially dedicated shark tourists make up the expenditures that are attributable to sharks.

We asked each tourist how important the chance of seeing sharks/rays was in their decision to come to the site (Likert scale of five points from -2 to 2). With regards to tourist experience, we gathered data on overall satisfaction levels with sharks and/or ray tourism activities (Likert-like scale of 1–10) and their likelihood of re-joining the trip in the future and recommending the trip to a friend (Likert scale of five points from -2 to 2), as these are established indicators of customer satisfaction and value (Mustika et al., 2013). We also asked tourists to rank their favorite marine animals to encounter from a range of options (i.e., whale shark, reef shark, manta ray, devil ray and sting ray), and gauged perceptions of crowding through questions relating to the number of other tourists in the area during the shark/ray tourism activity.

Respondents were approached on the street, or in hotels and dive shops, with questionnaires available in both English and Bahasa Indonesia. Data was collected by a team of six trained enumerators, fluent in both English and Bahasa Indonesia. The enumerators were accompanied by a field coordinator, who assisted with approaching tour operators and facilitated interactions with tourists. Between May and July 2017, 365 tourist questionnaires were completed across the six case study locations, i.e., Nusa Penida (Bali, n = 121), Labuan Bajo (Flores, East Nusa Tenggara, n = 111), Morotai (North Maluku, n = 13), Bunaken (North Sulawesi, n = 40), Gili Matra (Lombok, n = 65) and Derawan (East Kalimantan, n = 15).

We used interviewer-assisted questionnaires to survey local fishers living in/nearby the shark tourism sites to understand their livelihood options, fishing practices, and role in/perceptions of the tourism industry. Questions asked related to demographic information, financial dependency (e.g., household size and the presence of other bread-winners), the involvement of their household in the tourism industry, their willingness or desire to be involved in the tourism industry, their fishing activities, and the contribution of shark and/or ray fisheries to their income.

During May-July 2017 a total of 84 fishers questionnaires were completed across six locations: Nusa Penida (Bali, n = 18), Labuan Bajo (Flores, East Nusa Tenggara, n = 22), Morotai (North Maluku, n = 11), Bunaken (North Sulawesi, n = 15), Gili Matra (Lombok, n = 4) and Derawan (East Kalimantan, n = 6). Each fisher survey was conducted in villages adjacent to the tourist site. Surveys were conducted in Bahasa Indonesia by a team of six trained Indonesian enumerators.

We combined field- and desk-based data to estimate current and future annual economic value of shark and ray tourism in Indonesia through the following steps:

We estimated the number of shark tourists based on data from sampled operators. We use the national-level mean and median numbers of shark tourists for provinces that have no sampled-operators. The numbers are from sampled sites with the same travel effort characteristics to the unsampled sites. For instance, for an unsampled site with medium travel effort, we used the number of tourists from a sampled site with medium travel effort.

Each site’s annual total tourist expenditures are composed of the primary (dive-related) expenditures and the auxiliary direct expenditures multiplied by the number of days per trip per tourist, and the estimated number of tourist per site per annum. We estimated total annual tourist expenditures per case study site based on the number of dive operators per site, the estimated number of visitors per operator per year, and estimated tourist expenditures per person per trip (Tables 2, 3). We did not include airfares in the calculation of tourist expenditures.

Spatial and temporal scales are important considerations in choosing variables used in benefit transfer (Richardson et al., 2015). Particularly for a vast country like Indonesia, spatial variability needs to be taken into account when extrapolating the expenditures of sampled sites to the national level. To that end, we used two spatial variables as indicators for identifying suitable proxy values: travel effort as an indicator for assigning a proxy for number of visitors, and Purchasing Power Parity (PPP) as an indicator for assigning a proxy for unit expenditure.

The travel efforts were divided into three categories based on the site’s access to international airports (Table 4A):

We considered three variables to be selected as an indicator or a proxy of expenditure per tourist from the sampled sites to the unsampled sites. Since we sought to assign sites with comparable costs per person, we considered Consumer Price Index (CPI), PPP or the local minimum wages as the proxy. CPI is “a measure that tracks changes in the cost of a basket of goods and services purchased” by a typical household (Karjan et al., 2020, p. 287). CPI captures temporal changes in the cost of goods and service and, in general, changes in the average cost of living. PPP is a theory that “purchasing power in different countries should be the same when stated in a common currency,” but it almost never holds exactly (Karjan et al., 2020, p. 473). Substantially different prices are often observed between countries (a dollar in India purchases more goods than a dollar in Australia) and also within a country (a dollar in Bali might purchase very little compared to a dollar in a village in Central Java, though both are in Indonesia). Since PPP also applies to the within-country setting, Indonesia has a PPP index for every province in the country.

We chose PPP as the most suitable indicator, since CPI is a temporal comparison that represents inflation (Karjan et al., 2020), while minimum wage is influenced by political decisions. We used PPP per province from 2016 (Bps East Java, 2017) to identify the most suitable sampled sites as proxies for expenditure in non-sampled sites. For each non-sampled site, we identified a PPP among the six sampled sites that is closest to the unsampled site’s PPP. We then used the mean and median dive expenditures (EDIV) from the matched sampled site as proxies for the non-sampled sites. We included the ratio between the PPP of the unsampled site and the PPP of the sampled site as a correction factor for the expenditures of unsampled sites (PPP_S, PPP_U, and PPP_PROX, Tables 2, 3).

The auxiliary expenditures of sampled sites were calculated based on data on actual expenditure of tourists visiting those sites (EAUX in Table 3). For non-sampled sites, auxiliary expenditures from sampled sites were not used because of the risk of over-estimating the total expenditures. In particular, Gili Matra (West Nusa Tenggara) has a very high auxiliary expenditure (Table 1). Because Gili Matra is a proxy for expenditures for six non-sampled locations (Wakatobi, Weh Island, Botubarani, Moyo, Belongas, and Togian Island – see also Table 4A “Proxy” and “Purchasing Power Parity” columns), using the real auxiliary expenditures of Gili Matra for the six locations will likely to inflate the estimated total expenditures. Thus, we use the ratio of total expenditures of sampled sites and dive expenditures of sampled sites (RATIO in Table 3) to arrive at the total expenditures of unsampled sites (EXU_TOT in Table 3).

We then summed the total tourist expenditures across all sites (EXS_TOT for sampled sites and EXU_TOT proxies for unsampled) to estimate annual tourist expenditure per year for the whole of Indonesia (EX_TOT) based on number of visitors per operator per year (from dive operator online survey) and number of operators per site (from desk-based research) (Tables 2, 3). As a proxy for potential income loss, we used the ratio between hypothetical number of days lost in the absence of sharks/rays in that site and the actual trip length at the same site (D_SHARK in Table 3) to generate hypothetical expenditure loss (called “attributable expenditures,” EX_SHARK in Table 3) from the shark tourism industry. To make it easier for readers, we only report the attributable expenditures in the Result, although we still report the non-attributable dive and total expenditures in Tables 4B,C. We did not use the importance of sharks in site selection to determine attributable expenditures because we considered numbers (hypothetical days of stay without sharks/rays) as the more reliable variable, but we used this variable for tourist satisfaction analysis (see section Determinants of Tourist Satisfaction and Motivation to Visit.).

To estimate future tourism value, we assumed a constant tourism growth rate in Indonesia of 5.1% per annum over the next ten years based on the tourism growth rate in the ASEAN region for 2016 (Indonesia Travel, 2017). This is a conservative estimate, since national tourism growth for Indonesia alone in January to September 2017 was estimated at 17.5% (Indonesia Travel, 2017).

SPSS ver 25 was used to calculate tourist expenditures per person per trip from the six sampled sites. Microsoft Excel for Mac ver 15.27 was used to estimate annual tourist expenditures for all recorded operators in the 24 locations in Indonesia.

Very few interviewees from local communities were identified as shark fishers or being engaged in/receiving benefits from the tourism industry. As a result, there were too few samples for robust analytical statistics. Instead, we used descriptive statistics to interpret the results for understanding the role of shark tourism for communities.

We used SPSS ver. 25 to analyze the tourist satisfaction level and whether sharks or rays was a significant factor that influenced a tourist’s decision to visit a particular location. All predictors were tested individually before using multiple regressions as a model selection framework. Collinearity diagnostics (Field, 2013; IBM SPSS, 2020) were done to predictors before conducting the analyses (details of analyses in Results).

We identified 24 shark and ray tourism sites spread across 14 provinces in Indonesia (Figure 1 and Table 4). These sites range from popular, well-established tourist destinations with 70 dive operators per site (i.e., Nusa Penida, Tulamben, and Padang Bali in Bali) and more than 198,200 shark tourists per year (i.e., Gili Matra in West Nusa Tenggara) to more remote locations with just one or two operators and between 2,400 and 3,000 tourists per year (e.g., Banyuwangi in East Java and Cendrawasih Bay in West Papua, Figure 1 and Table 4). The tourism sites are predominantly concentrated in central and eastern Indonesia (Figure 1).

We have 45 dedicated shark tourists (12.5% of n = 361) and 63 partially dedicated shark tourists (17.5% of n = 361) in our dataset. Regardless of whether they were dedicated tourists or not, we estimated that around 772,171 shark tourists visited Indonesia in 2017 (median number; mean around 629,771 tourists in 2017, Table 4B). As such, the median number of dedicated and partially dedicated shark tourists were 188,931 tourists per annum (mean 231,650 tourists per annum). These tourists contribute to the attributable expenditures for the shark tourism industry.

The median total expenditures per person per trip to a given site averaged across all sites was USD 598.44 (mean USD 859.05) (Table 1). Of these total expenditures, shark, and ray tourism activity expenditures (including scuba diving, snorkeling and boating) make up at least 35% of all per person per trip expenditures (median USD 214.5, mean USD 370.16 per person per trip; Table 1). The median auxiliary expenditures per person per trip (meals, hotels, communication, souvenir and local transport) was USD 275 (mean USD 475.6) (Table 1).

Spending patterns across the six sampled locations were heterogeneous (Independent samples Kruskal–Wallis Test for total expenditure p < 0.001 at CI 95%, df = 5; Independent samples Median Test for total expenditure p = 0.001 at CI 95%; df = 5). The highest median scuba dive expenditures per person per trip was spent in Morotai (USD 400) (mean USD 360.31, Table 1). The highest median auxiliary expenditure per person per trip was spent in Derawan (USD 465) (mean 617.87, Table 1). The highest median total direct expenditures per person per trip was also in Derawan Archipelago (USD 1,039) (mean USD 1,061.8, Table 1).

We estimate that the median attributable shark and ray dive expenditures in 2017 was at least USD 22 million (mean USD 64.1 million, Table 4B). We estimate that the median total attributable expenditures in 2017 was USD 73.6 million (mean USD 296.3 million, Table 4C). This number contributed at least 7.37% (median value) of the total USD 1 billion marine tourism revenue in Indonesia in 2017 (Table 4C). These attributable expenditures also contribute to up to 24.9% of the total expenditures generated from shark/ray tourists (median value, Table 4C). This is the value that Indonesia’s dive industry as a whole could hypothetically lose in the absence of sharks.

Based on conservative 5.1% projected tourism growth rates, and assuming shark populations in key tourism sites are maintained at current levels or healthier, the median attributable dive expenditures of shark and ray tourists by 2027 are projected to be USD 36.28 million (mean USD 105.55 million, see also Figure 2). By 2027, the median total attributable expenditures of shark and ray tourists are projected to be 121.17 million (mean 263.86 million, Figure 2). This is the potential loss of income to Indonesia (non-inflation-adjusted) should shark and ray populations decline, leading to the loss of sharks from Indonesian waters/popular tourism sites.

A total of 79.2% respondents scored between 8 and 10 on the satisfaction scale (Figure 3). Two benchmarks are available for tourism satisfaction: Hanan and Karp (1989) and Pearce (2006). Hanan and Karp (1989) considered customer satisfaction as high if at least 85% respondents score between 8 and 10 (on the scale of 1–10), medium if between 70 and 80% respondents score 8–10 (the scale left out 80–85%), and low if lower than 60% respondents score 8–10 (the scale left out 60–70%). Pearce (2006) recommended low satisfaction for mean of <7.1, medium satisfaction between 7.1 and 7.8 and high satisfaction for mean of >7.8. Since our satisfaction data is ordinal and not normally distributed (Figure 3, also both Kolmogorov–Smirnov and Shapiro–Wilk p-values are 0.000), we refrain from using mean to analyze our satisfaction data. Thus, using Hanan and Karp (1989), the overall satisfaction levels with shark and ray tourism in Indonesia can be considered moderate. However, if considering the Pearce (2006) benchmark, bearing in mind that our satisfaction is not normally distributed, the satisfaction level of our respondents can be seen as moderate, edging toward high.

Up to 95.6% (43 out of 45) of dedicated shark tourists and 93.7% (59 out of 63) of partially dedicated shark tourists considered sharks/rays as an important factor in their decisions to visit a site. Since dedicated and partially dedicated shark tourists contributed to up to 24.9% of the shark tourist expenditures, we checked several demographic variables as potential predictors to whether sharks/rays were an important factor to visit the site using a series of individual Chi Square Independent tests. The variables are gender (nominal, dummy), whether it was a first visit (nominal, dummy), location (nominal), whether the tourist came from a western country (nominal, dummy) and education level (nominal, dummy). Using Logistic Regression, we analyzed the determinants of respondents’ satisfaction levels against age (interval). Location was the only variable with a significant association (Pearson Chi Square p = 0.000, Cramer’s V-value = 0.321, medium association). Nusa Penida, Komodo, and Lombok have significantly more tourists who stated that sharks/rays were important in their decision to come. Bunaken is the reversed, but effect is also significant (significantly fewer tourists stated that sharks/rays were important in their decision to come to Bunaken).

Using a series of individual Chi Square Independent tests, we analyzed the determinants of respondents’ satisfaction levels against 18 predictors (i.e., location (nominal), whether shark or ray was an important factor to visit the area (nominal, dummy), gender (nominal, dummy), whether it was their first visit to the area (nominal, dummy), the tourist’s perception about the number of boats around him/her (categorical, ordinal), whether the tourists were overseas visitors (nominal, dummy), whether the respondent was a westerner or not (nominal, dummy), whether respondents saw reef sharks, manta rays, devil rays, sting rays or whale sharks (nominal, all dummy variables), what rank of importance the respondents gave these animals (ordinal, dummy), and education level (ordinal, dummy). Using Logistic Regression, we analyzed the determinants of respondents’ satisfaction levels against two interval predictors (age and the length of stay expressed in the number of nights). We found six predictors with significant effects (whether the tourists saw reef sharks, manta rays, devil rays or sting rays, location and whether shark or ray was an important factor to visit the area, all p < 0.005).

We ran collinearity diagnostic on the six significant individually significant predictors. We detected no collinearity: no Eigenvalue was above 15 (the highest was 3.790), no VIF was greater than 10 (the highest was 1.749), mean VIF was not substantially greater than 1 (mean = 1.41), and no Condition Index was close to zero (the lowest was 1.000). We then re-run the analysis with Logistic Regression just for the six significant predictors.

Our best model identified two significant predictors (Model p = 0.000, Model Chi Square = 83.296, Block Chi Square = 34.076, Nagelkerke Rsqr = 0.352): (1) whether respondents saw reef sharks [p < 0.001, B = 1.287, SE = 0.340, Exp(B) = 3.621]; and (2) whether respondents saw manta rays [p < 0.001, B = 2.289, SE = 0.428, Exp(B) = 9.863]. The satisfaction of a tourist who saw a reef shark was likely to increase 3.6× compared to when s/he did not see reef shark. The satisfaction of a tourist who saw a manta ray was likely to increase 9.8× compared to when s/he did not see manta. Our finding is consistent with tourist responses on favorite animals to encounter, with whale sharks (n = 205, 63%), manta rays (n = 93; 28%) ranking most highly, followed by the reef sharks (n = 21; 6%) (Figure 4) (noting that whale sharks were very rarely seen in the sites we sampled, therefore it did not play in to the satisfaction determinants).

To explore differences between Western and non-Western shark tourists, we re-ran Logistic Regression for satisfaction analyses separately, also preceded by collinearity diagnostic. We found that Western tourists behaved similar to pooled results, with the same significant predictors (best model p = 0.000, Model Chi Square = 77.877, Block Chi Square = 65.785, Nagelkerke Rsqr = 0.378). The two significant predictors are: (1) whether respondents saw reef sharks [p = 0.002, B = 1.091, SE = 0.354, Exp(B) = 2.977]; and (2) whether respondents saw manta rays [p < 0.001, B = 2.650, SE = 0.365, Exp(B) = 14.158]. The satisfaction of a Western tourist who saw a reef shark was likely to increase ∼3× compared to when s/he did not see reef shark. The satisfaction of a Western tourist who saw a manta ray was likely to increase ∼14× compared to when s/he did not see manta.

When we analyzed the satisfaction determinants for non-Western shark tourists with Logistic Regression, we found a different pattern. With best model p = 0.000, Model Chi Square = 13.518, Block Chi Square = 13.518, Nagelkerke Rsqr = 0.369, the only significant predictor was the importance of sharks or rays in the tourists’ decision to come to a place [p = 0.001, B = 3.045, SE = 0.880, Exp(B) = 21.000]. If a non-Western tourist felt that sharks or rays were important in their decision to come to a place, their satisfaction is likely to increase 21× compared to if s/he didn’t think sharks or rays were important. Whether these tourists actually saw a shark or a ray was not significantly associated with their satisfaction level.

Examination of perceptions of crowding revealed that 42% of respondents considered the number of boats or tourists among them as “too many” or “far too many,” while about half considered the crowding “just about right” (Figure 5). We found a significant difference between crowding preferences of Western and non-Western tourists, with non-Western tourists more likely to consider that the number of boats/tourists around them was too few (Pearson chi square p = 0.003, Likelihood Ratio p = 0.013, value = 12.76, df = 4, n = 352). Unexpectedly, crowding (expressed in the tourist’s perception about the number of boats around him/her) was not a significant predictor in satisfaction level.

Our examination also revealed that tourists who ranked at least 8 out of 10 in the satisfaction level would be more likely to re-join the trip in the future (Pearson Chi Square p = 0.000, df = 1). The same type of tourists would also be more likely to recommend the trip to others (Pearson Chi Square p = 0.004, df = 1). Entailed in these analyses is the importance of the higher likelihood to see reef sharks or manta rays in tourist re-joining or recommending the trips, for the ability to see sharks or manta rays were significant to higher satisfaction.

Of all community respondents interviewed in/around the case study tourism sites, 75% reported capture fisheries as their main source of household income, while others were involved in fish trading or processing. Of these, only 8 out of 84 interviewed fishers (9.5%) reported sharks as a key target species, and only 1 respondent reported that shark fishing was a significant contributor to their livelihood.

Just 32% of interviewees (N = 27) either had worked or were currently working in the tourism sector at the time of the survey, and none of these identified as shark fishers present or past. Similarly, only 3 of the 11 dive operators who filled in the online questionnaires reported employing former shark fishers, although 67% of community respondents who didn’t work in the tourism sector reported that they aspired to (n = 76). Most tourism-related occupations for fisher community were lower-skill (i.e., requiring less re-training of individuals), focused on boat operations for taking tourists snorkeling and sightseeing. 17% of the fishing households (N = 14) reported other family members working in the tourism sector, also as lower-skilled workers such as cleaners. For the households engaged in tourism, tourism livelihoods contributed around half of the total household income.